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Dimensionality Reduction
This page contains resources about Dimensionality Reduction, Model Order Reduction , Blind Signal Separation, Source Separation and Subspace Learning. Subfields * Supervised Dimensionality Reduction ** Linear Discriminant Analysis (LDA) *** Fisher Linear Discriminant (FDA) * Unsupervised Dimensionality Reduction ** Singular Value Decomposition (SVD) ** Principal Component Analysis (PCA) ** Canonical-Correlation Analysis (CCA) ** Independent Component Analysis (ICA) ** Exploratory Factor Analysis (EFA) ** Singular Spectrum Analysis (SSA) ** Empirical Orthogonal Function (EOF) Analysis ** Non-negative Matrix Factorization (NNMF) ** Maximum-Margin (Minimum-Norm) Matrix Factorization ** Autoencoder * Nonlinear Dimensionality Reduction ** Manifold Learning * Canonical or Principal Angles between subspaces Online Courses Video Lectures *Dimensionality Reduction by Neil D. Lawrence *Lecture: Dimensionality reduction Using PCA by S. Sengupta *Lecture: Dimensionality Reduction by David Hogg *Dimensionality Reduction by Feature Selection in Machine Learning by Dunja Mladenić *Subspace Learning by Alessandro Rudi *Lecture: Nonlinear Dimensionality Reduction by Neil D. Lawrence Lecture Notes *Multivariate Analysis, Dimensionality Reduction, and Spectral Methods by Sham Kakade *Large Scale Learning by Sham Kakade and Greg Shakhnarovich *Mathematics for Data Science by Bowei Yan *Dimensionality Reduction by Andrzej Pronobis - with code *Lecture: Dim Reduction by Paris Smaragdis and Sarah E. King *Lecture: Dimension Reduction by Alan L. Yuille *Lecture: Dimensionality Reduction by Oxley Hall *Lecture: Dimensionality reduction (PCA, LDA) by Ricardo Gutierrez-Osuna *Lecture: Dimensionality reduction, Feature selection by Milos Hauskrecht *Lecture: Nonlinear Dimensionality reduction by Milos Hauskrecht *Lecture: Reducing Data Dimension by Tom M. Mitchell *Lecture: Dimensionality Reduction by Andrew Ng *Lecture: Dimensionality reduction by Nuno Vasconcelos *Lecture: Linear dimensionality reduction by Percy Liang *Lecture: Dimensionality Reduction by Sethu Vijayakumar *Lecture: Dimensionality Reduction by Shai Shalev-Shwartz *Lecture: The Curse of Dimensionality and PCA by Olga Veksler *Lecture: Dimensionality Reduction by Gwenn Englebienne *Lecture: Dimensionality reduction by Doina Precup *Lecture: Dimensionality Reduction by Javier Hernandez Rivera *Lecture: Unsupervised Learning by Andrew Zisserman *Lecture: Dimensionality Reduction by Euripides G.M Petrakis *Advanced Statistical Machine Learning by Stefanos Zafeiriou Books and Book Chapters * Shalev-Shwartz, S., & Ben-David, S. (2014). "Chapter 26: Dimensionality Reduction". Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press. * Sun, L., Ji, S., & Ye, J. (2013). Multi-Label Dimensionality Reduction. CRC Press. * Lu, H., Plataniotis, K. N., & Venetsanopoulos, A. (2013). Multilinear subspace learning: Dimensionality reduction of multidimensional data. CRC press. * Rajaraman, A., & Ullman, J. D. (2012). "Chapter 11: Dimensionality Reduction". Mining of Massive Datasets. Cambridge University Press. * Comon, P., & Jutten, C. (Eds.). (2010). Handbook of Blind Source Separation: Independent component analysis and applications. Academic press. * Gorban, A. N., Kégl, B., Wunsch, D. C., & Zinovyev, A. (2008). Principal Manifolds for Data Visualization and Dimension Reduction. Springer. * Ranjan, A. (2008). A'' ''New Approach for Blind Source Separation of Convolutive Sources. VDM Verlag. * Lee, J. A., & Verleysen, M. (2007). Nonlinear Dimensionality Reduction. Springer. * Skillicorn, D. (2007). Understanding complex datasets: data mining with matrix decompositions. CRC press. Scholarly Articles * Sorzano, C. O. S., Vargas, J., & Montano, A. P. (2014). A survey of dimensionality reduction techniques. arXiv preprint arXiv:1403.2877. * Baur, U., Benner, P., & Feng, L. (2014). Model order reduction for linear and nonlinear systems: a system-theoretic perspective. Archives of Computational Methods in Engineering, 21(4), 331-358. * Gu, C. (2011). Model order reduction of nonlinear dynamical systems. ''PhD Diss., University of California, Berkeley. * Burges, C. J. (2010). ''Dimension Reduction: A Guided Tour. Foundations and Trends® in Machine Learning, 4(3). Now Publishers Inc. * Cunningham, P. (2008). Dimension Reduction. In Machine Learning Techniques for Multimedia (pp. 91-112). Springer. * Fodor, I. K. (2002). A survey of Dimension Reduction Techniques. Tutorials *Dimensionality Reduction by Ali Ghodsi (2006) *Dimensionality Reduction the Probabilistic Way by Neil D. Lawrence (ICML 2008) *Dimensionality Reduction by Wei-Lun Chao (2011) *Dimensionality Reduction From Several Angles by (2013) Software *Toolbox for Dimensionality Reduction - MATLAB *Matlab codes for dimensionality reduction (subspace learning) *gensim - Python *scikit-learn (Dimension Reduction with PCA) - Python *Multifactor Dimensionality Reduction (MDR) See also * Deep Learning and Representation Learning * Dictionary Learning Other Resources *Dimensionality Reduction @ Toronto *Dimensionality reduction for sparse binary data - using gensim Python library Category:Machine Learning Category:Control Theory Category:Signal Processing